A framework for discovering meaningful associations in the annotated life sciences Web

During the last decade, life sciences researchers have gained access to the entire human genome, reliable high-throughput biotechnologies, affordable computational resources, and public network access. This has produced vast amounts of data and knowledge captured in the life sciences Web, and has created the need for new tools to analyze this knowledge and make discoveries. Consider a simplified Web of three publicly accessible data resources Entrez Gene, PubMed and OMIM. Data records in each resource are annotated with terms from multiple controlled vocabularies (CVs). The links between data records in two resources form a relationship between the two resources. Thus, a record in Entrez Gene, annotated with GO terms, can have links to multiple records in PubMed that are annotated with MeSH terms. Similarly, OMIM records annotated with terms from SNOMED CT may have links to records in Entrez Gene and PubMed. This forms a rich web of annotated data records. The objective of this research is to develop the Life Science Link (LSLink) methodology and tools to discover meaningful patterns across resources and CVs. In a first step, we execute a protocol to follow links, extract annotations, and generate datasets of termlinks, which consist of data records and CV terms. We then mine the termlinks of the datasets to find potentially meaningful associations between pairs of terms from two CVs. Biologically meaningful associations of pairs of CV terms may yield innovative nuggets of previously unknown knowledge. Moreover, the bridge of associations across CV terms will reflect the practice of how scientists annotate data across linked data repositories. Contributions include a methodology to create background datasets, metrics for mining patterns, applying semantic knowledge for generalization, tools for discovery, and validation with biological use cases. Inspired by research in association rule mining and linkage analysis, we develop two metrics to determine support and confidence scores in the associations of pairs of CV terms. Associations that have a statistically significant high score and are biologically meaningful may lead to new knowledge. To further validate the support and confidence metrics, we develop a secondary test for significance based on the hypergeometric distribution. We also exploit the semantics of the CVs. We aggregate termlinks over siblings of a common parent CV term and use them as additional evidence to boost the support and confidence scores in the associations of the parent CV term. We provide a simple discovery interface where biologists can review associations and their scores. Finally, a cancer informatics use case validates the discovery of associations between human genes and diseases

Developmental progress and current status of the Animal QTLdb

The Animal QTL Database (QTLdb; http://www.animalgenome.org/QTLdb) has undergone dramatic growth in recent years in terms of new data curated, data downloads and new functions and tools. We have focused our development efforts to cope with challenges arising from rapid growth of newly published data and end users’ data demands, and to optimize data retrieval and analysis to facilitate users’ research. Evidenced by the 27 releases in the past 11 years, the growth of the QTLdb has been phenomenal. Here we report our recent progress which is highlighted by addition of one new species, four new data types, four new user tools, a new API tool set, numerous new functions and capabilities added to the curator tool set, expansion of our data alliance partners and more than 20 other improvements. In this paper we present a summary of our progress to date and an outlook regarding future directions

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

Conceptualization of Computational Modeling Approaches and Interpretation of the Role of Neuroimaging Indices in Pathomechanisms for Pre-Clinical Detection of Alzheimer Disease

With swift advancements in next-generation sequencing technologies alongside the voluminous growth of biological data, a diversity of various data resources such as databases and web services have been created to facilitate data management, accessibility, and analysis. However, the burden of interoperability between dynamically growing data resources is an increasingly rate-limiting step in biomedicine, specifically concerning neurodegeneration. Over the years, massive investments and technological advancements for dementia research have resulted in large proportions of unmined data. Accordingly, there is an essential need for intelligent as well as integrative approaches to mine available data and substantiate novel research outcomes. Semantic frameworks provide a unique possibility to integrate multiple heterogeneous, high-resolution data resources with semantic integrity using standardized ontologies and vocabularies for context- specific domains. In this current work, (i) the functionality of a semantically structured terminology for mining pathway relevant knowledge from the literature, called Pathway Terminology System, is demonstrated and (ii) a context-specific high granularity semantic framework for neurodegenerative diseases, known as NeuroRDF, is presented. Neurodegenerative disorders are especially complex as they are characterized by widespread manifestations and the potential for dramatic alterations in disease progression over time. Early detection and prediction strategies through clinical pointers can provide promising solutions for effective treatment of AD. In the current work, we have presented the importance of bridging the gap between clinical and molecular biomarkers to effectively contribute to dementia research. Moreover, we address the need for a formalized framework called NIFT to automatically mine relevant clinical knowledge from the literature for substantiating high-resolution cause-and-effect models

의학 연구에서의 과학적 증거의 활용을 위한 시각적 분석 시스템 디자인

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022. 8. 서진욱.Evidence-based medicine, "the conscientious, explicit, and judicious use of current best evidence in healthcare and medical research" [98], is one of the most widely accepted medical paradigms of modern times. Searching, reviewing, and synthesizing reliable and high-quality scientific evidence is the key step for the paradigm. However, despite the widespread use of the EBM paradigm, challenges remain in applying Evidence-based medicine protocols to medical research. One of the barriers to applying the best scientific evidence to medical research is the severe literature and clinical data overload that causes the evidence-based tasks to be tremendous time-consuming tasks that require vast human effort. In this dissertation, we aim to employ visual analytics approaches to address the challenges of searching and reviewing massive scientific evidence in medical research. To overcome the burden and facilitate handling scientific evidence in medical research, we conducted three design studies and implemented novel visual analytics systems for laborious evidence-based tasks. First, we designed PLOEM, a novel visual analytics system to aid evidence synthesis, an essential step in Evidence-Based medicine, and generate an Evidence Map in a standardized method. We conducted a case study with an oncologist with years of evidence-based medicine experience. In the second study, we conducted a preliminary survey with 76 medical doctors to derive the design requirements for a biomedical literature search. Based on the results, We designed EEEVis, an interactive visual analytic system for biomedical literature search tasks. The system enhances the PubMed search result with several bibliographic visualizations and PubTator annotations. We performed a user study to evaluate the designs with 24 medical doctors and presented the design guidelines and challenges for a biomedical literature search system design. The third study presents GeneVis, a visual analytics system to identify and analyze gene expression signatures across major cancer types. A task that cancer researchers utilize to discover biomarkers in precision medicine. We conducted four case studies with domain experts in oncology and genomics. The study results show that the system can facilitate the task and provide new insights from the data. Based on the three studies of this dissertation, we conclude that carefully designed visual analytics approaches can provide an enhanced understanding and support medical researchers for laborious evidence-based tasks in medical research.근거중심의학(Evidence-Based Medicine)이란 "임상 치료 및 의학 연구에서 현재 존재하는 최고의 증거를 양심적이고, 명백하며, 분별 있게 이용하는 방법론"이며 [98], 현대 의학에서 가장 널리 받아들여지는 의학 패러다임이다. 신뢰할 수 있는 고수준의 과학적 근거를 검색, 검토, 합성하는 것이야 말로 근거중심의학의 핵심이다. 하지만, 근거중심의학이 이미 광범위하게 사용되고 있음에도 불구하고, 의학 연구에 근거중심의학의 프로토콜을 실천하는 데에는 여전히 많은 어려움이 따른다. 의료 문헌 정보, 임상 정보 및 유전체학 정보까지 연구자가 검토해야 할 근거의 양은 방대하며 광범위하다. 또한 의학과 기술의 발전으로 인해 점차 더 빠른 속도로 늘어나고 있기에, 이를 모두 엄밀히 검토하기 위해서는 막대한 양의 시간과 인력이 있어야 한다. 본 논문은 시각적 분석 방법론을 접목하여 의학 연구에서 방대한 과학적 증거를 검색하고 검토할 시 발생하는 막대한 인적 자원의 과부하 문제를 완화하고자 한다. 이를 위하여 근거중심의학의 절차 중 특히 인력 소모가 막심한 절차들을 선정하고, 이러한 난관을 극복하고 보다 효율적이고 효과적으로 데이터에서 유의미한 정보를 도출할 수 있게끔 보조하는 세 가지 시각적 분석 시스템들을 구현하였으며, 각각의 시스템에 관한 디자인 연구를 수행하였다. 우선 첫 디자인 연구에서는 근거중심의학 연구에 있어 필수적 단계인 근거 합성 방법론의 하나인 근거 매핑(Evidence Mapping) 과정을 지원하기 위한 시각적 분석 시스템 PLOEM을 설계했다. 그리고 이를 검증하기 위해 다년간의 근거 기반 의료 경험이 있는 종양학자와 함께 사례 연구를 수행했다. 두 번째 디자인 연구에서는 의학 문헌 검색 시스템의 요구사항 분석을 위해 총 76명의 의사를 상대로 설문조사를 진행하였고, 이러한 분석을 바탕으로 대화형 시각적 분석 시스템인 EEEVis를 설계했다. 이 시스템은 여러 종의 서지 정보 시각화 인터페이스와 PubTator의 주석 정보를 활용하여 PubMed 검색 엔진의 검색 결과를 증강하는 시스템이며, 이를 평가하기 위해 총 24명의 의사와 함께 사용자 연구를 수행하였다. 이 연구 결과를 바탕으로 의학 문헌 검색 시스템에 대한 설계 지침과 과제를 제시한다. 마지막으로 세 번째 디자인 연구에서는 임의의 유전자군의 유전자 발현 패턴을 주요 암 유형에 따라 시각화하고 분석할 수 있는 시스템인 GeneVis를 설계하였다. 암 유형에 따른 유전자 발현 패턴의 분석과 비교는 암 연구자들이 정밀 의학에서 생체 지표(Biomarker)를 발견하기 위해 빈번히 수행하는 작업이다. 우리는 종양학 전문가 및 유전체학 전문가 총 4인을 대상으로 사례 연구를 진행하였고, 그 결과 GeneVis가 해당 작업을 더 수월하게 수행하는 것과 기존의 데이터에서 새로운 정보를 도출하는 것에 도움이 되었음을 확인하였다. 위의 세 디자인 연구의 결과를 바탕으로, 본 논문은 사용자 분석과 작업 분석을 동반한 시각적 분석 방법론이 의학 연구의 근거 관련 작업의 어려움을 해소하고, 분석 데이터에 대한 보다 나은 이해를 제공하는 것이 가능하다고 결론 내린다.CHAPTER1 Introduction 1 1.1 Background and Motivation 1 1.2 Dissertation Outline 5 CHAPTER2 Related Work 7 2.1 Evidence Mapping: Graphical Representation for a Scientific Evidence Landscape 7 2.2 Scientific Literature Visualizations and Bibliography Visualizations 9 2.3 Visual Anlytics Systems for Genomics Data sets and Research Tasks 10 CHAPTER3 PLOEM: An Interactive Visualization Tool for Effective Evidence Mapping with Biomedical literature 12 3.1 Introduction 12 3.2 Visual Representations and Interactions of PLOEM 14 3.2.1 Overview of the PICO Criteria 14 3.2.2 Trend Visualization with the Timeline view 17 3.2.3 Representing the PICO Co-occurrence with the Relation view 20 3.2.4 Study detail view 22 3.3 Usage Scenarios: Visualizing Various Study Sizes with PLOEM 23 3.4 Conclusion 24 CHAPTER4 EEEvis: Efficacy improvement in searching MEDLINE database using a novel PubMed visual analytic system 26 4.1 Introduction 26 4.1.1 Motivation 26 4.1.2 Preliminary Survey: A Questionnaire on conventional literature search methods 28 4.1.3 Design Requirements for Biomedical Literature Search Systems 36 4.2 System and Interface Implementation of EEEVis 37 4.2.1 System Overview 37 4.2.2 Bibliography Filters 40 4.2.3 Timeline View 41 4.2.4 Co-authorship Network View 43 4.2.5 Article List and Detail View 44 4.3 User Study 46 4.3.1 Participants 46 4.3.2 Procedures 48 4.3.3 Results and Observations 50 4.4 Discussion 54 4.4.1 Design Implications 56 4.4.2 Limitations and Future Work 57 4.5 Conclusions 59 CHAPTER5 GeneVis: A Visual Analytics Systemfor Gene Signature Analysis in Cancers 68 5.1 Motivation 68 5.2 System and Interface Implementation 69 5.2.1 System Overview 69 5.2.2 Gene Expression Detail View 71 5.2.3 Gene Vector Projection View 72 5.2.4 Gene x Cancer Type Heatmap view 74 5.2.5 User Interaction in Multiple Coordinated Views 76 5.3 Case Studies 76 5.3.1 Participants 76 5.3.2 Task and Procedures 76 5.3.3 Case1: Identifying SimilarGeneSignatures with TGFB1in Hallmark Gene Sets 80 5.3.4 Case2: Identifying Cluster Patterns in the HRD data set 81 5.3.5 Results 82 5.4 Summary 85 CHAPTER6 Conclusion and future work 86 6.1 Conclusion 86 6.2 Future Work 87 Abstract (Korean) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102박

KnetMiner - An integrated data platform for gene mining and biological knowledge discovery

Hassani-Pak K. KnetMiner - An integrated data platform for gene mining and biological knowledge discovery. Bielefeld: Universität Bielefeld; 2017.Discovery of novel genes that control important phenotypes and diseases is one of the key challenges in biological sciences. Now, in the post-genomics era, scientists have access to a vast range of genomes, genotypes, phenotypes and ‘omics data which - when used systematically - can help to gain new insights and make faster discoveries. However, the volume and diversity of such un-integrated data is often seen as a burden that only those with specialist bioinformatics skills, but often only minimal specialist biological knowledge, can penetrate. Therefore, new tools are required to allow researchers to connect, explore and compare large-scale datasets to identify the genes and pathways that control important phenotypes and diseases in plants, animals and humans. KnetMiner, with a silent "K" and standing for Knowledge Network Miner, is a suite of open-source software tools for integrating and visualising large biological datasets. The software mines the myriad databases that describe an organism’s biology to present links between relevant pieces of information, such as genes, biological pathways, phenotypes and publications with the aim to provide leads for scientists who are investigating the molecular basis for a particular trait. The KnetMiner approach is based on 1) integration of heterogeneous, complex and interconnected biological information into a knowledge graph; 2) text-mining to enrich the knowledge graph with novel relations extracted from literature; 3) graph queries of varying depths to find paths between genes and evidence nodes; 4) evidence-based gene rank algorithm that combines graph and information theory; 5) fast search and interactive knowledge visualisation techniques. Overall, [KnetMiner](http://knetminer.rothamsted.ac.uk) is a publicly available resource that helps scientists trawl diverse biological databases for clues to design better crop varieties and understand diseases. The key strength of KnetMiner is to include the end user into the “interactive” knowledge discovery process with the goal of supporting human intelligence with machine intelligence

The Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) framework for understanding musicality-language links across the lifespan

Using individual differences approaches, a growing body of literature finds positive associations between musicality and language-related abilities, complementing prior findings of links between musical training and language skills. Despite these associations, musicality has been often overlooked in mainstream models of individual differences in language acquisition and development. To better understand the biological basis of these individual differences, we propose the Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) framework. This novel integrative framework posits that musical and language-related abilities likely share some common genetic architecture (i.e., genetic pleiotropy) in addition to some degree of overlapping neural endophenotypes, and genetic influences on musically and linguistically enriched environments. Drawing upon recent advances in genomic methodologies for unraveling pleiotropy, we outline testable predictions for future research on language development and how its underlying neurobiological substrates may be supported by genetic pleiotropy with musicality. In support of the MAPLE framework, we review and discuss findings from over seventy behavioral and neural studies, highlighting that musicality is robustly associated with individual differences in a range of speech-language skills required for communication and development. These include speech perception-in-noise, prosodic perception, morphosyntactic skills, phonological skills, reading skills, and aspects of second/foreign language learning. Overall, the current work provides a clear agenda and framework for studying musicality-language links using individual differences approaches, with an emphasis on leveraging advances in the genomics of complex musicality and language traits

Doctor of Philosophy

dissertationThe objective of this work is to examine the efficacy of natural language processing (NLP) in summarizing bibliographic text for multiple purposes. Researchers have noted the accelerating growth of bibliographic databases. Information seekers using traditional information retrieval techniques when searching large bibliographic databases are often overwhelmed by excessive, irrelevant data. Scientists have applied natural language processing technologies to improve retrieval. Text summarization, a natural language processing approach, simplifies bibliographic data while filtering it to address a user's need. Traditional text summarization can necessitate the use of multiple software applications to accommodate diverse processing refinements known as "points-of-view." A new, statistical approach to text summarization can transform this process. Combo, a statistical algorithm comprised of three individual metrics, determines which elements within input data are relevant to a user's specified information need, thus enabling a single software application to summarize text for many points-of-view. In this dissertation, I describe this algorithm, and the research process used in developing and testing it. Four studies comprised the research process. The goal of the first study was to create a conventional schema accommodating a genetic disease etiology point-of-view, and an evaluative reference standard. This was accomplished through simulating the task of secondary genetic database curation. The second study addressed the development iv and initial evaluation of the algorithm, comparing its performance to the conventional schema using the previously established reference standard, again within the task of secondary genetic database curation. The third and fourth studies evaluated the algorithm's performance in accommodating additional points-of-view in a simulated clinical decision support task. The third study explored prevention, while the fourth evaluated performance for prevention and drug treatment, comparing results to a conventional treatment schema's output. Both summarization methods identified data that were salient to their tasks. The conventional genetic disease etiology and treatment schemas located salient information for database curation and decision support, respectively. The Combo algorithm located salient genetic disease etiology, treatment, and prevention data, for the associated tasks. Dynamic text summarization could potentially serve additional purposes, such as consumer health information delivery, systematic review creation, and primary research. This technology may benefit many user groups